DeepDMC: A Traffic Context Independent Deep Driving Maneuver Classification Framework

Connected and autonomous vehicles have been introduced to increase roadway safety and traffic flow efficiency, where an autonomous vehicle shares its current and near-future driving maneuver information using different CAN-bus signals (e.g., speed, brake pedal pressure, and so on) with its nearby vehicles using wireless communication technologies. In order to classify these driving maneuver data, deep neural network (DNN) models are widely utilized as DNN models result in high prediction accuracy. However, the existing DNN-based classification models are not suitable for the driving maneuver time-series datasets, which are often imbalanced (i.e., different number of instances for different types of maneuvers). The raw driving signals also carry (univanted) traffic context information (e.g., congestions, signs, curvatures, etc.), which can significantly reduce the driving maneuver classification accuracy. This paper presents a Deep Driving Maneuver Classification (DeepDMC) framework to classify different driving maneuvers more accurately. There are three modules in the proposed framework: maneuver classification, context discrimination, and maneuver generation. First, the maneuver classification module takes both labeled and unlabeled data to predict different maneuvers. Second, the context discriminator module detects different traffic context information from the data, and then reduces traffic contexts’ impacts to classify different driving maneuvers. Third, the maneuver generation module generates synthetic maneuver data to handle the imbalanced data problem. To better understand the proposed framework’s robustness, we evaluate the proposed framework with other existing DNN-based classification models using two real driving datasets. From the experimental evaluations, we find that the proposed DeepDMC framework outperforms other existing classification models by up to 42.82% and 37.53% in terms of precision and recall values, respectively.